OmniEdit

In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality.

📃Paper | 🌐Website | 💻Github | 📚Dataset

Dataset Columns

The dataset contains the following columns:

• src, edited_img: they are the source and edited images.
• edited_prompt_list: they are the short and long editing instructions.
• task: this indicates the editing task, which has seven categories like addition, removal, background, environment, style, etc.
• sc_score_1 and sc_score_1: semantic consistency score assigned by our quality rater.
• pq_score: the perceptual quality score assigned by our quality rater.
• o_score: the overall score, which is the weighted average of sc and pq score.
• *_reasoning: the rationale for assigning these scores.

Data Pipeline

We synthesize the large scale dataset through specialist distillation. Our synthesis pipeline is depicted in

Our released version contains 1.2M pairs covering seven different skills like addition, swaping, removal, attribute modification, background change, environment change and sytle transfer. The dataset has been filtered with VIEScore.

Comparison with Others

Our dataset has the most diverse, highest-quality image editing pairs of any resolution.

Citation

If you find our paper useful, please cite us with

@article{wei2024omniedit,
  title={OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision},
  author={Wei, Cong and Xiong, Zheyang and Ren, Weiming and Du, Xinrun and Zhang, Ge and Chen, Wenhu},
  journal={arXiv preprint arXiv:2411.07199},
  year={2024}
}